What is reinforcement learning? How AI Trains

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What are useful open source options for reinforcement learning? How do major vendors handle reinforcement learning? How do AI startups handle reinforcement learning? Is there anything reinforcement learning can't do?

Machine learning (ML) can be considered the central subset of artificial intelligence (AI), and reinforcement learning can be the quintessential subset of ML that people imagine when they think AI.

Reinforcement learning is the process by which a machine learning algorithm, robot, etc. can be programmed to respond to complex, real-time, real-world environments to optimally achieve a desired goal or outcome. Consider the challenge posed by self-driving cars.

The algorithms involved can also "learn" from, or be improved by, this process of taking into account and responding to new circumstances.

Other forms of ML can be "trained" by sometimes massive sets of "training data", often allowing an algorithm to classify or group data (or recognize patterns) based on relationships and results on which they were trained. Machine learning algorithms start by training data and create models that capture some of the patterns and lessons embedded in the data.

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Reinforcement learning is part of the training process that often occurs after deployment when the model works. New data captured from the environment is used to refine and adjust the model to the current world.

Reinforcement learning is accomplished with a feedback loop based on "rewards" and "penalties". The scientist or user creates a list of pass and fail results, then the AI ​​uses them to fit the model. This may change some of the weights in the model, or even re-evaluate some or all of the training data in light of the new reward or penalty.

For example, a self-driving car might have a set of simple rewards and penalties that are predetermined. The algorithm gets a reward if it arrives on time and does not make sudden gear changes like emergency braking or rapid acceleration. If the car hits the curb, gets into a bad traffic jam, or brakes unexpectedly, the algorithm is penalized. The model can be recycled with careful attention to the process that led to the bad results.

In some cases, reinforcement occurs during and after real-world deployment. In other cases, the model is refined in a simulation that generates synthetic events that can reward or penalize the algorithm. These simulations are particularly useful with systems like autonomous vehicles that are expensive and dangerous to test in real deployment.

In many cases, reinforcement learning is just an extension of the main learning algorithm. It iterates through the same process over and over again after the model is used. The stages are similar, and the rewards and punishments are part of a long s...